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Data Mining Techniques

There are a few data mining techniques that can be used by an organization but the type of data being examined strongly influences the type of data mining technique to be used.

It is very important to remember that the nature of data mining is constantly evolving and new DM techniques are being implemented all the time.

Although there are many techniques, the main ones used by data mining software includes but not limited to clustering, classification, regression and association methods.

Clustering:

This is the formation of data clusters that are grouped together by some sort of relationship that identifies that data as being similar. An example of this would be sales data that is clustered into specific markets.

Classification:

In this method, data is grouped together by applying known structure to the data warehouse being examined. The method is great for categorical information and uses one or more algorithms such as decision tree learning, neural networks and "nearest neighbor" methods.

Regression:

Regression utilizes mathematical formulas and is superb for numerical information. It simply looks at the numerical data and then attempts to apply a formula that fits that data.

Then new data can be plugged into the formula, which results in predictive analysis.

Association:

Also known as "association rule learning," this particular method is popular and entails the discovery of interesting relationships between variables in the data warehouse (where the data is stored for analysis). Once an association "rule" has been established, predictions can then be made and acted upon. An example of this is shopping: if people buy a particular item then there may be a high chance that they also buy another specific item (the store manager could then make sure these items are located near each other).

Data Mining and the Business Intelligence Part:

Business intelligence simply refers to the gathering and analyzing and storing of data for the purpose of making smart business decisions. Business intelligence is commonly divided into several layers, all of which constitute the business intelligence "stack."

The analytics layer is responsible for data analysis and it is this layer where data mining occurs within the stack. The other elements that are part of the analytics layer are predictive analysis and KPI (key performance indicator) formation.

Data mining is a critical part of business intelligence and providing key relationships between different groups of data that is then displayed to end users via data visualization (part of the BI stack's presentation layer). Individuals can then quickly view these relationships in a graphical manner and take some sort of action based on the data being displayed.